Data streaming applications in Cyber-Physical Systems enable high-throughput, low-latency transformations of raw data into value. The performance of such applications, run by Stream Processing Engines (SPEs), can be boosted through custom CPU scheduling. Previous schedulers in the literature require alterations to SPEs to control the scheduling through user-level threads. While such alterations allow for fine-grained control, they hinder the adoption of such schedulers due to the high implementation cost and potential limitations in application semantics (e.g., blocking I/O). Motivated by the above, we explore the feasibility and benefits of custom scheduling without alterations to SPEs but, instead, by orchestrating the OS scheduler (e.g., using nice and cgroup) to enforce the scheduling goals. We propose Lachesis, a standalone scheduling middleware, decoupled from any specific SPE, that can schedule multiple streaming applications, run in one or many nodes, and possibly multiple SPEs. Our evaluation with real-world and synthetic workloads, several SPEs and hardware setups, shows its benefits over default OS scheduling and other state-of-the-art schedulers: up to 75% higher throughput, and 1130x lower average latency once such SPEs reach their peak processing capacity.